MicroRNAs (miRNAs) are critical post-Transcriptional regulators of gene expression. Due to challenges in accurate profiling of small RNAs, a vast majority of public transcriptome datasets lack reliable miRNA profiles. However, the biological consequence of miRNA activity in the form of altered protein-coding gene (PCG) expression can be captured using machine-learning algorithms. Here, we present iMIRAGE (imputed miRNA activity from gene expression), a convenient tool to predict miRNA expression using PCG expression of the test datasets. The iMIRAGE package provides an integrated workflow for normalization and transformation of miRNA and PCG expression data, along with the option to utilize predicted miRNA targets to impute miRNA activity from independent test PCG datasets.
Bibliographical noteFunding Information:
This work was supported by an NIH/NCI grant [1R01CA204856-01A1], and a research grant from the Avon Foundation for Women.
PubMed: MeSH publication types
- Journal Article
- Research Support, N.I.H., Extramural
- Research Support, Non-U.S. Gov't